Real-Time Feature Engineering and Model Serving Architecture using Databricks Delta Live Tables. 9. 746-758. 10.32628

The proliferation of real-time machine learning applications has necessitated sophisticated feature engineering frameworks capable of processing streaming data with minimal latency while maintaining data quality and consistency. This paper presents a comprehensive architecture for real-time feature engineering and model serving utilizing Databricks Delta Live Tables (DLT) as the foundational streaming data processing engine. Our proposed system addresses critical challenges in feature freshness, data quality assurance, and automated model adaptation within production environments. Through extensive experimentation across Azure and AWS cloud platforms, we demonstrate significant improvements in feature computation latency, model serving performance, and operational efficiency. The architecture incorporates automated feature drift detection mechanisms, dynamic feature selection algorithms, and continuous model retraining pipelines that adapt to evolving data patterns. Experimental results indicate a 40% improvement in feature freshness metrics and a 25% reduction in model inference latency compared to traditional batch-based feature engineering approaches. The system successfully handles high-throughput streaming scenarios while maintaining sub-second response times for model predictions, making it suitable for mission-critical applications requiring real-time decision making.